BESearch: A Supervised Learning Approach to Search for Molecular Event Participants

Biomedical researchers rely on keyword-based search engines to retrieve superficially relevant documents, from which they must filter out irrelevant information manually. Hence, there is an urgent need for a more efficient system to help them rapidly locate specific molecular events and the participants involved in these events. In this paper, we propose a novel search system with a new search interface and answer ranking scheme. Due to the limited number of query types in the Biomedical-specific searches, we employ a form-based interface with various query templates for specifying required information. This can ascertain a user’s intentions more accurately than a conventional keyword-based interface. Ranking is another key issue in this type of search. We propose a linear ranking model, trained by a supervised learning algorithm, which combines different features. Two semantic features, named entity types and semantic roles, are incorporated into the model to help match a query wit...

• Type: Conference
• Year: 2007
• Where: IRI
• Authors: Richard Tzong-Han Tsai, Hong-Jie Dai, Hsi-Chuan Hung, Ryan T. K. Lin, Wen-Chi Chou, Ying-Shan Su, Min-Yuh Day, Wen-Lian Hsu
• Keywords: Information Retrieval, IRI 2007, Keyword-based Search Engines, Relevant Documents, Semantic Features